Agent-based modeling is a discrete-event, object-oriented, spatially-explicit type of computer simulation that is an increasingly popular modeling method for converting the correlations identified from Big Data into dynamic representations of mechanistic knowledge. Agent-based models (ABMs) representing populations of interacting cells have been used to examine a range of physiological/pathophysiological systems such as cancer, sepsis, infectious disease, wound healing, and gastrointestinal disease. A natural step in the evolution of agent-based modeling is the desire to develop high-resolution, anatomic-scale organ ABMs that can reproduce recognizable clinical pathophysiology. However, the operational challenge of effectively parameterizing (calibrating), characterizing (meta-modeling) and validating such models are daunting, if not computationally intractable as a practical issue, given existing methods. To help address this issue, we propose to utilize automated adaptive simulation workflows on anatomic-level multi-scale agent-based models to enable and make tractable the process of exploring the parameter and behavior spaces of very large (hundreds of billions of agents) ABMs able to represent entire organ systems. These workflows, already tested in the characterization of smaller scale ABMs, will be extended to state-of- the-art high performance computing (HPC) environments in order to demonstrate and eventually provide this capability to researchers developing larger and more complex ABMs, fundamentally changing how such models are used and analyzed. We will utilize a high-performance version of the Swift task-parallel scripting language (Swift/T), to perform parameterization and behavior-space exploration of an enhanced version of the Spatially Explicit General-purpose Model of Enteric Tissue (SEGMEnT) HPC implemented at anatomic scale, i.e., the entire small and large intestine. This project includes performing parameter-space characterization of SEGMEnT HPC at multiple scaling levels using previously identified objective functions derived from tissue- and organ-level features of intestinal tissue, porting SEGMEnT HPC to Repast HPC, an existing HPC-capable ABM toolkit. In doing so we will expand Repast HPC's ability to represent complex biological phenomena, and develop adaptive simulation workflows using Swift/T and Repast HPC, tested in the Repast HPC implementation of SEGMEnT, in order to facilitate parameter space exploration and characterization by the general modeling community.